




"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase				:     int					=				logging.get_logger(__name__)

lowerCAmelCase				:     int					=				{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}



def        a__					(							snake_case__			,					snake_case__			,					snake_case__			,					snake_case__			,					snake_case__							)		->   int:
			for attribute in key.split("""."""							):
						lowerCamelCase      =   getattr(snake_case__			,					snake_case__							)

			if weight_type is not None:
						lowerCamelCase      =   getattr(snake_case__			,					snake_case__							).shape
			else:
						lowerCamelCase      =   hf_pointer.shape

			assert hf_shape == value.shape, (
			    F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
			    F' {value.shape} for {full_name}'
			)

			if weight_type == "weight":
						lowerCamelCase      =   value
			elif weight_type == "weight_g":
						lowerCamelCase      =   value
			elif weight_type == "weight_v":
						lowerCamelCase      =   value
			elif weight_type == "bias":
						lowerCamelCase      =   value
			else:
						lowerCamelCase      =   value

			logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'							)



def        a__					(							snake_case__			,					snake_case__			,					snake_case__							)		->   str:
			lowerCamelCase      =   []
			lowerCamelCase      =   fairseq_model.state_dict()

			lowerCamelCase      =   hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						lowerCamelCase      =   False
						if "conv_layers" in name:
									load_conv_layer(
									    snake_case__			,					snake_case__			,					snake_case__			,					snake_case__			,					hf_model.config.feat_extract_norm == """group"""			,					)
									lowerCamelCase      =   True
						else:
									for key, mapped_key in MAPPING.items():
												lowerCamelCase      =   """hubert.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

												if key in name or (key.split("""w2v_model."""							)[-1] == name.split("""."""							)[0] and not is_finetuned):
															lowerCamelCase      =   True
															if "*" in mapped_key:
																		lowerCamelCase      =   name.split(snake_case__							)[0].split("""."""							)[-2]
																		lowerCamelCase      =   mapped_key.replace("""*"""			,					snake_case__							)
															if "weight_g" in name:
																		lowerCamelCase      =   """weight_g"""
															elif "weight_v" in name:
																		lowerCamelCase      =   """weight_v"""
															elif "weight" in name:
																		lowerCamelCase      =   """weight"""
															elif "bias" in name:
																		lowerCamelCase      =   """bias"""
															else:
																		lowerCamelCase      =   None
															set_recursively(snake_case__			,					snake_case__			,					snake_case__			,					snake_case__			,					snake_case__							)
												continue
						if not is_used:
									unused_weights.append(snake_case__							)

			logger.warning(F'Unused weights: {unused_weights}'							)



def        a__					(							snake_case__			,					snake_case__			,					snake_case__			,					snake_case__			,					snake_case__							)		->   str:
			lowerCamelCase      =   full_name.split("""conv_layers."""							)[-1]
			lowerCamelCase      =   name.split("""."""							)
			lowerCamelCase      =   int(items[0]							)
			lowerCamelCase      =   int(items[1]							)

			if type_id == 0:
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
									    F'{full_name} has size {value.shape}, but'
									    F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
									)
									lowerCamelCase      =   value
									logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'							)
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
									    F'{full_name} has size {value.shape}, but'
									    F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
									)
									lowerCamelCase      =   value
									logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'							)
			elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
									    F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
									    " found."
									)
									lowerCamelCase      =   value
									logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
									    F'{full_name} has size {value.shape}, but'
									    F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
									)
									lowerCamelCase      =   value
									logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
			else:
						unused_weights.append(snake_case__							)



@torch.no_grad()
def        a__					(							snake_case__			,					snake_case__			,					snake_case__=None			,					snake_case__=None			,					snake_case__=True							)		->   Union[str, Any]:
			if config_path is not None:
						lowerCamelCase      =   HubertConfig.from_pretrained(snake_case__							)
			else:
						lowerCamelCase      =   HubertConfig()

			if is_finetuned:
						if dict_path:
									lowerCamelCase      =   Dictionary.load(snake_case__							)

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									lowerCamelCase      =   target_dict.pad_index
									lowerCamelCase      =   target_dict.bos_index
									lowerCamelCase      =   target_dict.eos_index
									lowerCamelCase      =   len(target_dict.symbols							)
									lowerCamelCase      =   os.path.join(snake_case__			,					"""vocab.json"""							)
									if not os.path.isdir(snake_case__							):
												logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(snake_case__							)							)
												return
									os.makedirs(snake_case__			,					exist_ok=snake_case__							)
									with open(snake_case__			,					"""w"""			,					encoding="""utf-8"""							) as vocab_handle:
												json.dump(target_dict.indices			,					snake_case__							)
									lowerCamelCase      =   WavaVecaCTCTokenizer(
									    snake_case__			,					unk_token=target_dict.unk_word			,					pad_token=target_dict.pad_word			,					bos_token=target_dict.bos_word			,					eos_token=target_dict.eos_word			,					word_delimiter_token="""|"""			,					do_lower_case=snake_case__			,					)
									lowerCamelCase      =   True if config.feat_extract_norm == """layer""" else False
									lowerCamelCase      =   WavaVecaFeatureExtractor(
									    feature_size=1			,					sampling_rate=1_60_00			,					padding_value=0			,					do_normalize=snake_case__			,					return_attention_mask=snake_case__			,					)
									lowerCamelCase      =   WavaVecaProcessor(feature_extractor=snake_case__			,					tokenizer=snake_case__							)
									processor.save_pretrained(snake_case__							)

						lowerCamelCase      =   HubertForCTC(snake_case__							)
			else:
						lowerCamelCase      =   HubertModel(snake_case__							)

			if is_finetuned:
						lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase      =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]			,					arg_overrides={"""data""": """/""".join(dict_path.split("""/"""							)[:-1]							)}							)
			else:
						lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase      =   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							)

			lowerCamelCase      =   model[0].eval()

			recursively_load_weights(snake_case__			,					snake_case__			,					snake_case__							)

			hf_wavavec.save_pretrained(snake_case__							)


if __name__ == "__main__":
					lowerCAmelCase				:     List[str]					=				argparse.ArgumentParser()
					parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
					parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
					parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
					parser.add_argument(
					    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
					)
					lowerCAmelCase				:     Optional[int]					=				parser.parse_args()
					convert_hubert_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)









"""simple docstring"""




import warnings

from .generation import TFGenerationMixin






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				# warning at import time
				warnings.warn(
				    "Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
				    "be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead."		,   UpperCAmelCase__		,   )



